Generative Adversarial Network Image Synthesis Method for Skin Lesion Generation and Classification

Freedom Mutepfe, Behnam Kiani Kalejahi, Saeed Meshgini, Sebelan Danishvar

DOI: 10.4103/jmss.JMSS_53_20

Abstract


Background: One of the common limitations in the treatment of cancer is in the early detection of this disease. The customary medical practice of cancer examination is a visual examination by the dermatologist followed by an invasive biopsy. Nonetheless, this symptomatic approach is time-consuming and prone to human errors. An automated machine learning model is essential to capacitate fast diagnoses and early treatment. Objective: The key objective of this study is to establish a fully automatic model that helps Dermatologists in skin cancer handling process in a way that could improve skin lesion classification accuracy. Method: The work is conducted following an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) using the Python-based deep learning library Keras. We incorporated effective image filtering and enhancement algorithms such as bilateral filter to enhance feature detection and extraction during training. The Deep Convolutional Generative Adversarial Network (DCGAN) needed slightly more fine-tuning to ripe a better return. Hyperparameter optimization was utilized for selecting the best-performed hyperparameter combinations and several network hyperparameters. In this work, we decreased the learning rate from the default 0.001 to 0.0002, and the momentum for Adam optimization algorithm from 0.9 to 0.5, in trying to reduce the instability issues related to GAN models and at each iteration the weights of the discriminative and generative network were updated to balance the loss between them. We endeavour to address a binary classification which predicts two classes present in our dataset, namely benign and malignant. More so, some well-known metrics such as the receiver operating characteristic -area under the curve and confusion matrix were incorporated for evaluating the results and classification accuracy. Results: The model generated very conceivable lesions during the early stages of the experiment and we could easily visualise a smooth transition in resolution along the way. Thus, we have achieved an overall test accuracy of 93.5% after fine-tuning most parameters of our network. Conclusion: This classification model provides spatial intelligence that could be useful in the future for cancer risk prediction. Unfortunately, it is difficult to generate high quality images that are much like the synthetic real samples and to compare different classification methods given the fact that some methods use non-public datasets for training.


Keywords


DCGAN, dermoscopy, pretraining, skin lesion

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Hay RJ, Augustin M, Griffiths C, Sterry W, Board of the International League of Dermatological Societies and the Grand Challenges Consultation groups. The global challenge for skin health. Br J Dermatol 2015;172:1469-72.

Lopez AR, Giro-i-Nieto X, Burdick J, Marques O. Skin lesion classification from dermoscopic images using deep learning techniques. 13th IASTED Int Conf Biomed Eng (BioMed) 2017. p. 49-54.

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: NIPS'14: NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014;2:2672–80.

Dong HW, Yang YH. Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation. Taipei, Taiwan: Research Center for IT Innovation, Academia Sinica;

Yap J, Yolland W, Tschandl P. Multimodal skin lesion classification using deep learning. Exp Dermatol 2018;27:1261-7.

Aldwgeri A, Abubacker NF. Ensemble of Deep Convolutional Neural Network for Skin Lesion Classification in Dermoscopy Images . International Visual Informatics Conference, IVIC 2019: Advances in Visual Informatics. p. 214-26.

Khan MQ, Hussain A, Rehman Su, Khan U, Maqsood M, Mehmood K, et al. Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer. July 2019IEEE Access PP(99):1-1, DOI: 10.1109/ACCESS.2019.2926837.

Pham TC, Luong CM, Visani M, Hoang VD. Deep CNN and data augmentation for skin lesion classification. Intell Inf Database Syst Lect Notes Comput Sci 2018;10752:573-82.

Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 2016;35:1285-98.

Karabulut E, Ibrikci T. Texture analysis of melanoma images for computer-aided diagnosis. Int Conf Intell Comput Comput Sci Inform Sys (ICCSIS 16) 2016;2:26-9.

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-8.

Yang X, Zeng Z, Yeo SY, Tan C, Tey HL, Yi Su. A novel multitask deep learning model for skin lesion segmentation and classification. arxive 2017;1:10-5.

Nasr-Esfahani E, Samavi S, Karimi N, Soroushmehr SM, Jafari MH, Ward K, et al. Melanoma detection by analysis of clinical images using convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc 2016;2016:1373-6.

Hosny KM, Kassem MA, Foaud MM. Classification of skin lesions using transfer learning and augmentation with Alex-net. PLoS One 2019;14:e0217293.

Radford A, Metz L, Chintala S. Unsupervised Representation Learning,” Under Review as a Conference Paper at ICLR 2016 Indico Research; 7 January, 2016:3-15.

Denton E, Chintala S, Szlam A, Fergus R. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. New York: Facebook AI Research; 2015. p. 12-20.

Ahirwar K. Build next-generation generative models using TensorFlow. In: Generative Adversarial Projects. Packt Publishing Ltd; 2019. p. 89-95.

Tsang SH. “Review: DCGAN –Deep Convolutional Generative Adversarial Network (GAN).” Available from: https://medium.com/@sh.tsang/review-dcgan-deep-convolutional-generative-adversarial-network-gan-ec390cded63c. [Last accessed on 2020 Apr 25].

Nishad G. “MNIST-GAN: Detailed Step by Step Explanation & Implementation In Code,” INTEL AI ACADEMY. Available from: https://medium.com/intel-student-ambassadors/mnist-gan-detailed-step-by-step-explanation-implementation-in-code-ecc93b22dc60. [Last accessed on 2018 Aug 01].

Chen F, Chen N, Mao H, Hu H. Assessing four neural networks on handwritten digit recognition dataset (MNIST). Chuangxinban J Comput 2018.

Qin Z, Liu Z, Zhu P, Xue Y. A GAN-based image synthesis method for skin lesion classification. Comput Methods Programs Biomed 2020;195:105568.

Cronin J, Finni T, Seynnes O. Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images. Computer Methods and Programs in Biomedicine 2020;196:DOI:10.1016/j.cmpb.2020.105583.

Ghassemi N, Shoeibi A, Rouhani M. Deep neural network with generative adversarial networkspre-training for brain tumor classification based on MR images. Biomed Signal Process Control 2020; 57: DOI: 10.1016/j.bspc.2019.101678.

Github, “Github,” Github; 31 Jan 2018. Available from: https://github.com/4thgen/DCGAN-CIFAR10. [Last accessed on 2020 May 26].

Kaggle, “Kaggle,” Kaggle; 20 Sep 2016. Available from: https://www.kaggle.com/c/cifar-10. [Last accessed on 2020 May 10].

Fanconi C, “Kaggle,” Kaggle; 28 november 2018. Available from: https://www.kaggle.com/tatsukiishikawa/skin-cancer-classification. [Last accessed on 2020 May 14].

Gedraite ES, Hadad M. Investigation on the effect of a gaussian blur in image filtering and segmentation. Proceedings ELMAR-2011.

Adlam B, Weill C, Kapoor A. Investigating under and overfitting in wasserstein generative adversarial networks. arxive.org, 2019;2:12-22.

JB, Kingma DP. Adam: A Method for Stochastic Optimization. Published As a Conference Paper at ICLR 2015; 2017.

Desai U. “Training a Conditional DC-GAN on CIFAR-10,” Medium; 08 June 2018. Available from: https://medium.com/@utk.is.here/training-a-conditional-dc-gan-on-cifar-10-fce88395d610. [Last accessed on 2020 Apr 20].

Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 2018;29:1836-42.

Yu L, Chen H, Dou Q, Qin J, Heng PA. Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks. IEEE Transactions on Medical Imaging 2017;36:DOI:10.1109/TMI.2016.2642839.

Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems 30. NeurIPS Proceedings Search 2017:2:9-15.

Ozkan IA, Koklu M. Skin Lesion Classification using Machine Learning Algorithms. IJISAE 2017;5:285-9.

Moffy Crispin Vas D. Classification of benign and malignant lung nodules using image processing techniques. Int Res J Eng Technol 2017;4:DOI:10.1088/1361-6560/ab2544.

Lei B, Xia Z, Jiang F, Jiang X, Ge Z, Xu Y, et al. Skin lesion segmentation via generative adversarial networks with dual discriminators. Medical Image Analysis 2020;64:10.1016/j.media.2020.101716.

Fang W, Zhang F, Sheng VS, Ding Y. A method for improving CNN-based image recognition using DCGAN. Computers Materials and Continua 2018;57:167-78.

Liu S, Yu M, Li M, Xu Q. The research of virtual face based on Deep Convolutional Generative Adversarial Networks using Tensorflow. Physica A: Statistical Mechanics and its Applications 2019;521:667-80.

Haputhanthri U. “Medium,” The AI Team; 17 MAY, 2020. Available from: https://mc.ai/introduction-to-deep-convolutional-generative-adversarial-networks-using-pytorch/. [Last accessed on 2020 May 25].

Yadav V, Kaushik VD. A study on automatic early detection of skin cancer. Int J Advanced Intelligence Paradigms 2019;12, Nos. 3/4, 2019

World Cancer Research Fund International, “WCRF.Org;” 2018. Available from: http//www.wcrf.org. [Last accessed on 2020 May 03].

Email MP, Simson W, Guha A, Rüdiger R, Christian G, Navab WN. Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness, International Conference on Information Processing in Medical Imaging IPMI 2019: Information Processing in Medical Imaging p. 517-29.

Hwang U, Choi S, Lee HB, Yoon S. Adversarial training for disease prediction from electronic health records with missing data. arXiv preprint arXiv:1711.04126


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